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A study of two direction weighted (2D)2LDA for finger vein recognition

机译:双向加权(2D) 2 LDA的手指静脉识别研究

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摘要

To carry out the finger vein recognition effectively and quickly, according to the characteristics of horizontal and vertical two-dimensional linear discriminant analysis ((2D)2LDA) reducing the dimensions, an algorithm of finger vein recognition is proposed. The algorithm is two direction weighted (2D)2LDA ((W2D)2LDA) based on preprocessing image of the figure vein image. Firstly, the original finger vein image is pre-processed. Secondly, the normalized vein image is gotten. Finally, the algorithm of this paper is used to be recognized. The effect of the rate of cumulate eigenvalue for 2DLDA and (2D)2LDA is analyzed. The effect of the weighted value and the rate of cumulate eigenvalue on W2DLDA, W(2D)2LDA and (W2D)2LDA are analyzed as well. Experimental results on our database of finger vein images show that the method presented achieves high recognition accuracy. The redundant information of eigenvectors extracted by (2D)2LDA is restrained strongly, and the bi-direction weighted effect is better than the one direction weighted effect. The average recognition rate of (W2D)2LDA is higher than 2DLDA, W2DLDA, (2D)2LDA and W(2D)2LDA.
机译:为了有效而快速地进行手指静脉识别,根据水平和垂直二维线性判别分析((2D) 2 LDA)缩小尺寸的特点,提出了一种手指静脉识别算法。建议的。该算法是基于图形静脉图像的预处理图像的两个方向加权(2D) 2 LDA((W2D) 2 LDA)。首先,对原始手指静脉图像进行预处理。其次,获得归一化的静脉图像。最后,本文的算法被用于识别。分析了2DLDA和(2D)2LDA的累积特征值速率的影响。还分析了加权值和累积特征值率对W2DLDA,W(2D) 2 LDA和(W2D) 2 LDA的影响。在我们的手指静脉图像数据库上的实验结果表明,所提出的方法具有很高的识别精度。 (2D) 2 LDA提取的特征向量的冗余信息受到了较强的抑制,双向加权效果优于单向加权效果。 (W2D) 2 LDA的平均识别率高于2DLDA,W2DLDA,(2D) 2 LDA和W(2D) 2 LDA 。

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